Attribute and instance weighted naive Bayes
作者：
Highlights：
• Many different categories of approaches have been proposed to improve naive Bayes.
• Few works simultaneously pay attention to attribute weighting and instance weighting.
• We propose attribute and instance weighted naive Bayes (AIWNB) in this paper.
• To learn AIWNB, we propose an eager and a lazy algorithms: AIWNBE and AIWNBL.
• The experimental results validate the effectiveness of the proposed algorithms.
摘要
•Many different categories of approaches have been proposed to improve naive Bayes.•Few works simultaneously pay attention to attribute weighting and instance weighting.•We propose attribute and instance weighted naive Bayes (AIWNB) in this paper.•To learn AIWNB, we propose an eager and a lazy algorithms: AIWNBE and AIWNBL.•The experimental results validate the effectiveness of the proposed algorithms.
论文关键词：Naive Bayes,Attribute weighting,Instance weighting,Eager learning,Lazy learning
论文评审过程：Received 21 December 2019, Revised 24 June 2020, Accepted 22 September 2020, Available online 25 September 2020, Version of Record 25 September 2020.
论文官网地址：https://doi.org/10.1016/j.patcog.2020.107674
